Correlation analysis and systemic risk measurement of regional, financial and global stock indices

The systemic risk of financial systems can affect the entirety of stock markets around the world. As the time series of stock markets contain rich information of the corresponding complex systems, a novel method using the complex network theory is proposed to measure the systemic risk in stock markets. Through the correlation analysis the time series of stock market financial indicators can be converted to the series of the complex networks. The dynamic topological indices of the networks can be used to analyze the network transmission characteristics and calculate the systemic risk. Based on the network dynamics we construct and test the systemic risk measurement model from the perspectives of regional, financial and global stock indices respectively. The topological parameter model is introduced to measure the systemic risk and the comparison is made with the traditional measurement model. The results show that the new model can provide more detailed and accurate information on the systemic risk of stock markets. This method can be applied in giving suggestions on investment decisions and early warnings of systemic risk.